PP.test                  package:ts                  R Documentation

_P_h_i_l_l_i_p_s-_P_e_r_r_o_n _T_e_s_t _f_o_r _U_n_i_t _R_o_o_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Computes the Phillips-Perron test for the null hypothesis that 'x'
     has a unit root against a stationary alternative.

_U_s_a_g_e:

     PP.test(x, lshort = TRUE)

_A_r_g_u_m_e_n_t_s:

       x: a numeric vector or univariate time series.

  lshort: a logical indicating whether the short or long version of the
          truncation lag parameter is used.

_D_e_t_a_i_l_s:

     The general regression equation which incorporates a constant and
     a linear trend is used and the corrected t-statistic for a first
     order autoregressive coefficient equals one is computed.  To
     estimate 'sigma^2' the Newey-West estimator is used.  If 'lshort'
     is 'TRUE', then the truncation lag parameter is set to
     'trunc(4*(n/100)^0.25)', otherwise 'trunc(12*(n/100)^0.25)' is
     used.  The p-values are interpolated from Table 4.2, page 103 of
     Banerjee _et al._ (1993).

     Missing values are not handled.

_V_a_l_u_e:

     A list with class '"htest"' containing the following components: 

statistic: the value of the test statistic.

parameter: the truncation lag parameter.

 p.value: the p-value of the test.

  method: a character string indicating what type of test was
          performed.

data.name: a character string giving the name of the data.

_A_u_t_h_o_r(_s):

     A. Trapletti

_R_e_f_e_r_e_n_c_e_s:

     A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry
     (1993) _Cointegration, Error Correction, and the Econometric
     Analysis of Non-Stationary Data_, Oxford University Press, Oxford.

     P. Perron (1988) Trends and random walks in macroeconomic time
     series. _Journal of Economic Dynamics and Control_ *12*, 297-332.

_E_x_a_m_p_l_e_s:

     x <- rnorm(1000)
     PP.test(x)
     y <- cumsum(x) # has unit root
     PP.test(y)

